Tutorial: Fitting and evaluating mixed models using lme4

Douglas Bates, Department of Statistics, University of Wisconsin - Madison, USA


Mixed-effects models or, more simply, mixed models are statistical models that incorporate both fixed-effects parameters, which apply to an entire population or to well-defined subsets of a population, and random effects, which apply to specific experimental or observational units in the study. The workshop will introduce mixed-effects models and the lme4 package for fitting, analyzing and displaying linear mixed-effects models, generalized linear mixed models and nonlinear mixed models with scalar or vector-valued random effects in nested, crossed or partially crossed configurations. We will use recently developed capabilities in lme4 that allow for hypothesis testing on and interval estimation of the model parameters using profiled likelihood.


Topics will include:

Potential attendees

Those who wish to learn how to fit mixed-effects models using lme4.

Related link

The presenter is a co-author (with Jose Pinheiro) of Mixed-Effects Models in S and S-PLUS, Springer, 2000.

Tutorial Materials

Slides and other materials are found here.